Norming of the Tampa Scale for Kinesiophobia across pain diagnoses and various countries
Bibliographic record
Abstract
The present study aimed to develop norms for the Tampa Scale for Kinesiophobia (TSK), a frequently used measure of fear of movement/(re)injury. Norms were assessed for the TSK total score as well as for scores on the previously proposed TSK activity avoidance and TSK somatic focus scales. Data from Dutch, Canadian, and Swedish pain samples were used (N=3082). Norms were established using multiple regression to obtain more valid and reliable norms than can be obtained by subgroup analyses based on age or gender. In the Dutch samples (N=2236), pain diagnosis was predictive of all TSK scales. More specifically, chronic low back pain displayed the highest scores on the TSK scores followed by upper extremity disorder, fibromyalgia, and osteoarthritis. Gender was predictive of TSK somatic focus scores and age of TSK activity avoidance scores, with male patients having somewhat higher scores than female patients and older patients having higher scores compared with younger patients. In the Canadian (N=510) and Swedish (N=336) samples, gender was predictive of all TSK scales, with male patients having somewhat higher scores than female patients. These norm data may assist the clinician and researcher in the process of decision making and treatment evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".